Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-25T23:58:10.967Z Has data issue: false hasContentIssue false

11 - Structural Brain Imaging of Intelligence

from Part III - Neuroimaging Methods and Findings

Published online by Cambridge University Press:  11 June 2021

By
Stefan Drakulich  and
Sherif Karama
Edited by
Aron K. Barbey ,
Sherif Karama  and
Richard J. Haier
Aron K. Barbey
Affiliation:
University of Illinois, Urbana-Champaign
Sherif Karama
Affiliation:
McGill University, Montréal
Richard J. Haier
Affiliation:
University of California, Irvine
Get access

Summary

The brain’s remarkable inter-individual structural variability provides a wealth of information that is readily accessible via structural Magnetic Resonance Imaging (sMRI). sMRI enables various structural properties of the brain to be captured on a macroscale level – one that is quickly moving towards submillimeter resolution (Budde, Shajan, Scheffler, & Pohmann, 2014; Stucht et al., 2015). This constitutes a remarkable leap forward from historically crude brain measures, such as head circumference measurements, aimed at understanding the neurobiology of intelligence differences.

Type
Chapter
Information
The Cambridge Handbook of Intelligence and Cognitive Neuroscience , pp. 210 - 234
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage Publications, Inc.Google Scholar
Aleman-Gomez, Y., Janssen, J., Schnack, H., Balaban, E., Pina-Camacho, L., Alfaro-Almagro, F., … Desco, M. (2013). The human cerebral cortex flattens during adolescence. Journal of Neuroscience, 33(38), 15004–15010.Google Scholar
Andreasen, N. C., Flaum, M., Swayze, V., 2nd, O’Leary, D. S., Alliger, R., Cohen, G., … Yuh, W. T. (1993). Intelligence and brain structure in normal individuals. American Journal of Psychiatry, 150(1), 130–134.Google Scholar
Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry – The methods. Neuroimage, 11(6 Pt 1), 805–821.CrossRefGoogle ScholarPubMed
Aydin, K., Ucar, A., Oguz, K. K., Okur, O. O., Agayev, A., Unal, Z., Yilmaz, S., and Ozturk, C. (2007). Increased gray matter density in the parietal cortex of mathematicians: A voxel-based morphometry study. AJNR American Journal of Neuroradiology, 28(10), 1859–1864.Google Scholar
Bajaj, S., Raikes, A., Smith, R., Dailey, N. S., Alkozei, A., Vanuk, J. R., & Killgore, W. D. S. (2018). The relationship between general intelligence and cortical structure in healthy individuals. Neuroscience, 388, 36–44.Google Scholar
Bassett, D. S., Bullmore, E., Verchinski, B. A., Mattay, V. S., Weinberger, D. R., & Meyer-Lindenberg, A. (2008). Hierarchical organization of human cortical networks in health and schizophrenia. Journal of Neuroscience, 28(37), 9239–9248.Google Scholar
Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 10–27.CrossRefGoogle Scholar
Bedford, S. A., Park, M. T. M., Devenyi, G. A., Tullo, S., Germann, J., Patel, R., … Consortium, Mrc Aims (2020). Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder. Molecular Psychiatry, 25(3), 614–628.Google Scholar
Bjuland, K. J., Løhaugen, G. C., Martinussen, M., & Skranes, J. (2013). Cortical thickness and cognition in very-low-birth-weight late teenagers. Early Human Development, 89(6), 371–380.CrossRefGoogle ScholarPubMed
Bourgeois, J. P., Goldman-Rakic, P. S., & Rakic, P. (1994). Synaptogenesis in the prefrontal cortex of rhesus monkeys. Cerebral Cortex, 4(1), 78–96.Google Scholar
Breslau, N., Chilcoat, H. D., Susser, E. S., Matte, T., Liang, K.-Y., & Peterson, E. L. (2001). Stability and change in children’s intelligence quotient scores: A comparison of two socioeconomically disparate communities. American Journal of Epidemiology, 154(8), 711–717.Google Scholar
Brouwer, R. M., Hedman, A. M., van Haren, N. E. M., Schnack, H. G., Brans, R. G. H., Smit, D. J. A., … Hulshoff Pol, H. E. (2014). Heritability of brain volume change and its relation to intelligence. Neuroimage, 100, 676–683.Google Scholar
Budde, J., Shajan, G., Scheffler, K., & Pohmann, R. (2014). Ultra-high resolution imaging of the human brain using acquisition-weighted imaging at 9.4T. Neuroimage, 86, 592–598.Google Scholar
Burgaleta, M., Johnson, W., Waber, D. P., Colom, R., & Karama, S. (2014). Cognitive ability changes and dynamics of cortical thickness development in healthy children and adolescents. Neuroimage, 84, 810–819.CrossRefGoogle ScholarPubMed
Burgaleta, M., MacDonald, P. A., Martínez, K., Román, F. J., Álvarez-Linera, J., Ramos González, A., … Colom, R. (2014). Subcortical regional morphology correlates with fluid and spatial intelligence. Human Brain Mapping, 35(5), 1957–1968.Google Scholar
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376.Google Scholar
Chance, S. A., Casanova, M. F., Switala, A. E., & Crow, T. J. (2008). Auditory cortex asymmetry, altered minicolumn spacing and absence of ageing effects in schizophrenia. Brain, 131(Pt 12), 3178–3192.Google Scholar
Chen, Z. J., He, Y., Rosa-Neto, P., Germann, J., & Evans, A. C. (2008). Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cerebral Cortex, 18(10), 2374–2381.Google Scholar
Chklovskii, D. B., Mel, B. W., & Svoboda, K. (2004). Cortical rewiring and information storage. Nature, 431(7010), 782–788.Google Scholar
Choi, Y. Y., Shamosh, N. A., Cho, S. H., DeYoung, C. G., Lee, M. J., Lee, J. M., … Lee, K. H. (2008). Multiple bases of human intelligence revealed by cortical thickness and neural activation. Journal of Neuroscience, 28(41), 10323–10329.CrossRefGoogle ScholarPubMed
Cocosco, C. A., Zijdenbos, A. P., & Evans, A. C. (2003). A fully automatic and robust brain MRI tissue classification method. Medical Image Analysis, 7(4), 513–527.Google Scholar
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18(2), 192–205.Google Scholar
Colom, R., Burgaleta, M., Román, F. J., Karama, S., Alvarez-Linera, J., Abad, F. J., … Haier, R. J. (2013). Neuroanatomic overlap between intelligence and cognitive factors: Morphometry methods provide support for the key role of the frontal lobes. Neuroimage, 72, 143–152.Google Scholar
Colom, R., Haier, R. J., Head, K., Álvarez-Linera, J., Quiroga, M. Á., Shih, P. C., & Jung, R. E. (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model. Intelligence, 37(2), 124–135.Google Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2006). Distributed brain sites for the g-factor of intelligence. Neuroimage, 31(3), 1359–1365.CrossRefGoogle ScholarPubMed
DeFelipe, J. (2011). The evolution of the brain, the human nature of cortical circuits, and intellectual creativity. Frontiers in Neuroanatomy, 5, 29.Google Scholar
Dubois, J., Galdi, P., Paul, L. K., & Adolphs, R. (2018). A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1756), 20170284.Google Scholar
Ducharme, S., Albaugh, M. D., Nguyen, T. V., Hudziak, J. J., Mateos-Perez, J. M., Labbe, A., … Brain Development Cooperative Group (2016). Trajectories of cortical thickness maturation in normal brain development – The importance of quality control procedures. Neuroimage, 125, 267–279.Google Scholar
Eickhoff, S. B., Constable, R. T., & Yeo, B. T. T. (2018). Topographic organization of the cerebral cortex and brain cartography. Neuroimage, 170, 332–347.Google Scholar
Escorial, S., Román, F. J., Martínez, K., Burgaleta, M., Karama, S., & Colom, R. (2015). Sex differences in neocortical structure and cognitive performance: A surface-based morphometry study. Neuroimage, 104, 355–365.Google Scholar
Estrada, E., Ferrer, E., Román, F. J., Karama, S., & Colom, R. (2019). Time-lagged associations between cognitive and cortical development from childhood to early adulthood. Developmental Psychology, 55(6), 1338–1352.Google Scholar
Evans, A. C., & Brain Development Cooperative Group (2006). The NIH MRI study of normal brain development. Neuroimage, 30(1), 184–202.CrossRefGoogle ScholarPubMed
Evans, A. C., Janke, A. L., Collins, D. L., & Baillet, S. (2012). Brain templates and atlases. Neuroimage, 62(2), 911–922.Google Scholar
Fjell, A. M., Westlye, L. T., Amlien, I., Tamnes, C. K., Grydeland, H., Engvig, A., … Walhovd, K. B. (2015). High-expanding cortical regions in human development and evolution are related to higher intellectual abilities. Cerebral Cortex, 25(1), 26–34.Google Scholar
Flashman, L. A., Andreasen, N. C., Flaum, M., & Swayze, V. W. (1997). Intelligence and regional brain volumes in normal controls. Intelligence, 25(3), 149–160.Google Scholar
Frangou, S., Chitins, X., & Williams, S. C. (2004). Mapping IQ and gray matter density in healthy young people. Neuroimage, 23(3), 800–805.Google Scholar
Ganjavi, H., Lewis, J. D., Bellec, P., MacDonald, P. A., Waber, D. P., Evans, A. C., … Brain Development Cooperative Group (2011). Negative associations between corpus callosum midsagittal area and IQ in a representative sample of healthy children and adolescents. PLoS One, 6(5), e19698.CrossRefGoogle Scholar
Gautam, P., Anstey, K. J., Wen, W., Sachdev, P. S., & Cherbuin, N. (2015). Cortical gyrification and its relationships with cortical volume, cortical thickness, and cognitive performance in healthy mid-life adults. Behavioural Brain Research, 287, 331–339.Google Scholar
Goh, S., Bansal, R., Xu, D., Hao, X., Liu, J., & Peterson, B. S. (2011). Neuroanatomical correlates of intellectual ability across the life span. Developmental Cognitive Neuroscience, 1(3), 305–312.Google Scholar
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage, 14(1 Pt 1), 21–36.Google Scholar
Green, S., Blackmon, K., Thesen, T., DuBois, J., Wang, X., Halgren, E., & Devinsky, O. (2018). Parieto-frontal gyrification and working memory in healthy adults. Brain Imaging Behavior, 12(2), 303–308.Google Scholar
Gregory, M. D., Kippenhan, J. S., Dickinson, D., Carrasco, J., Mattay, V. S., Weinberger, D. R., & Berman, K. F. (2016). Regional variations in brain gyrification are associated with general cognitive ability in humans. Current Biology, 26(10), 1301–1305.Google Scholar
Gur, R. C., Turetsky, B. I., Matsui, M., Yan, M., Bilker, W., Hughett, P., & Gur, R. E. (1999). Sex differences in brain gray and white matter in healthy young adults: Correlations with cognitive performance. Journal of Neuroscience, 19(10), 4065–4072.Google Scholar
Haier, R. J. (2016). The neuroscience of intelligence. Cambridge University Press.Google Scholar
Haier, R. J., Jung, R. E., Yeo, R. A., Head, K., & Alkire, M. T. (2004). Structural brain variation and general intelligence. Neuroimage, 23(1), 425–433.Google Scholar
Haier, R. J., Jung, R. E., Yeo, R. A., Head, K., & Alkire, M. T. (2005). The neuroanatomy of general intelligence: Sex matters. Neuroimage, 25(1), 320–327.Google Scholar
Haier, R. J., Karama, S., Colom, R., Jung, R., & Johnson, W. (2014). Yes, but flaws remain. Intelligence, 46, 341–344.Google Scholar
He, Y., Chen, Z. J., & Evans, A. C. (2007). Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral Cortex, 17(10), 2407–2419.Google Scholar
Hogstrom, L. J., Westlye, L. T., Walhovd, K. B., & Fjell, A. M. (2013). The structure of the cerebral cortex across adult life: Age-related patterns of surface area, thickness, and gyrification. Cerebral Cortex, 23(11), 2521–2530.Google Scholar
Huttenlocher, P. R. (1990). Morphometric study of human cerebral cortex development. Neuropsychologia, 28(6), 517–527.Google Scholar
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–154.Google Scholar
Kabani, N., Le Goualher, G., MacDonald, D., & Evans, A. C. (2001). Measurement of cortical thickness using an automated 3-D algorithm: A validation study. Neuroimage, 13(2), 375–380.Google Scholar
Karama, S., Ad-Dab’bagh, Y., Haier, R. J., Deary, I. J., Lyttelton, O. C., Lepage, C., … Brain Development Cooperative Group (2009). Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds. Intelligence, 37(2), 145–155.Google Scholar
Karama, S., Bastin, M. E., Murray, C., Royle, N. A., Penke, L., Muñoz Maniega, S., … Deary, I. J. (2014). Childhood cognitive ability accounts for associations between cognitive ability and brain cortical thickness in old age. Molecular Psychiatry, 19(5), 555–559.Google Scholar
Karama, S., Colom, R., Johnson, W., Deary, I. J., Haier, R., Waber, D. P., … Brain Development Cooperative Group (2011). Cortical thickness correlates of specific cognitive performance accounted for by the general factor of intelligence in healthy children aged 6 to 18. Neuroimage, 55(4), 1443–1453.Google Scholar
Kennedy, D. N., Lange, N., Makris, N., Bates, J., Meyer, J., & Caviness, V. S. Jr. (1998). Gyri of the human neocortex: An MRI-based analysis of volume and variance. Cerebral Cortex, 8(4), 372–384.Google Scholar
Khundrakpam, B. S., Reid, A., Brauer, J., Carbonell, F., Lewis, J., Ameis, S., … Brain Development Cooperative Group (2013). Developmental changes in organization of structural brain networks. Cerebral Cortex, 23(9), 2072–2085.Google Scholar
Kim, J. S., Singh, V., Lee, J. K., Lerch, J., Ad-Dab’bagh, Y., MacDonald, D., … Evans, A. C. (2005). Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage, 27(1), 210–221.Google Scholar
la Fougere, C., Grant, S., Kostikov, A., Schirrmacher, R., Gravel, P., Schipper, H. M., … Thiel, A. (2011). Where in-vivo imaging meets cytoarchitectonics: The relationship between cortical thickness and neuronal density measured with high-resolution [18F]flumazenil-PET. Neuroimage, 56(3), 951–960.Google Scholar
Lemaitre, H., Goldman, A. L., Sambataro, F., Verchinski, B. A., Meyer-Lindenberg, A., Weinberger, D. R., & Mattay, V. S. (2012). Normal age-related brain morphometric changes: Nonuniformity across cortical thickness, surface area and gray matter volume? Neurobiology of Aging, 33(3), 617.e1–617.e9.Google Scholar
Lenroot, R. K., Gogtay, N., Greenstein, D. K., Wells, E. M., Wallace, G. L., Clasen, L. S., … Giedd, J. N. (2007). Sexual dimorphism of brain developmental trajectories during childhood and adolescence. Neuroimage, 36(4), 1065–1073.Google Scholar
Lerch, J. P., & Evans, A. C. (2005). Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage, 24(1), 163–173.Google Scholar
Lerch, J. P., Worsley, K., Shaw, W. P., Greenstein, D. K., Lenroot, R. K., Giedd, J., & Evans, A. C. (2006). Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage, 31(3), 993–1003.Google Scholar
Li, W., Yang, C., Shi, F., Wu, S., Wang, Q., Nie, Y., & Zhang, X. (2017). Construction of individual morphological brain networks with multiple morphometric features. Frontiers in Neuroanatomy, 11, 34.Google Scholar
Lo, C. Y., He, Y., & Lin, C. P. (2011). Graph theoretical analysis of human brain structural networks. Reviews Neuroscience, 22(5), 551–563.Google Scholar
Luders, E., Narr, K. L., Bilder, R. M., Thompson, P. M., Szeszko, P. R., Hamilton, L., & Toga, A. W. (2007). Positive correlations between corpus callosum thickness and intelligence. Neuroimage, 37(4), 1457–1464.Google Scholar
Luders, E., Narr, K. L., Thompson, P. M., & Toga, A. W. (2009). Neuroanatomical correlates of intelligence. Intelligence, 37(2), 156–163.Google Scholar
Luders, E., Thompson, P. M., Narr, K. L., Zamanyan, A., Chou, Y. Y., Gutman, B., … Toga, A. W. (2011). The link between callosal thickness and intelligence in healthy children and adolescents. Neuroimage, 54(3), 1823–1830.Google Scholar
Lyttelton, O. C., Karama, S., Ad-Dab’bagh, Y., Zatorre, R. J., Carbonell, F., Worsley, K., & Evans, A. C. (2009). Positional and surface area asymmetry of the human cerebral cortex. Neuroimage, 46(4), 895–903.Google Scholar
MacDonald, D., Kabani, N., Avis, D., & Evans, A. C. (2000). Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage, 12(3), 340–356.Google Scholar
MacDonald, P. A., Ganjavi, H., Collins, D. L., Evans, A. C., & Karama, S. (2014). Investigating the relation between striatal volume and IQ. Brain Imaging and Behavior, 8(1), 52–59.Google Scholar
McDaniel, M. A. (2005). Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence, 33(4), 337–346.Google Scholar
Menary, K., Collins, P. F., Porter, J. N., Muetzel, R., Olson, E. A., Kumar, V., … Luciana, M. (2013). Associations between cortical thickness and general intelligence in children, adolescents and young adults. Intelligence, 41(5), 597–606.Google Scholar
Modroño, C., Navarrete, G., Nicolle, A., González-Mora, J. L., Smith, K. W., Marling, M., & Goel, V. (2019). Developmental grey matter changes in superior parietal cortex accompany improved transitive reasoning. Thinking & Reasoning, 25(2), 151–170.Google Scholar
Moffitt, T. E., Caspi, A., Harkness, A. R., & Silva, P. A. (1993). The natural history of change in intellectual performance: Who changes? How much? Is it meaningful? Journal of Child Psychology and Psychiatry, 34(4), 455–506.Google Scholar
Narr, K. L., Woods, R. P., Thompson, P. M., Szeszko, P., Robinson, D., Dimtcheva, T., … Bilder, R. M. (2007). Relationships between IQ and regional cortical gray matter thickness in healthy adults. Cerebral Cortex, 17(9), 2163–2171.Google Scholar
Panizzon, M. S., Fennema-Notestine, C., Eyler, L. T., Jernigan, T. L., Prom-Wormley, E., Neale, M., … Kremen, W. S. (2009). Distinct genetic influences on cortical surface area and cortical thickness. Cerebral Cortex, 19(11), 2728–2735.Google Scholar
Paradiso, S., Andreasen, N. C., O’Leary, D. S., Arndt, S., & Robinson, R. G. (1997). Cerebellar size and cognition: Correlations with IQ, verbal memory and motor dexterity. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 10(1), 1–8.Google Scholar
Paul, E. J., Larsen, R. J., Nikolaidis, A., Ward, N., Hillman, C. H., Cohen, N. J., … Barbey, A. K. (2016). Dissociable brain biomarkers of fluid intelligence. Neuroimage, 137, 201–211.Google Scholar
Paus, T., Zijdenbos, A., Worsley, K., Collins, D. L., Blumenthal, J., Giedd, J. N., … Evans, A. C. (1999). Structural maturation of neural pathways in children and adolescents: In vivo study. Science, 283(5409), 1908–1911.Google Scholar
Pietschnig, J., Penke, L., Wicherts, J. M., Zeiler, M., & Voracek, M. (2015). Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean? Neuroscience & Biobehavioral Reviews, 57, 411–432.Google Scholar
Rakic, P. (1988). Specification of cerebral cortical areas. Science, 241(4862), 170–176.Google Scholar
Raznahan, A., Shaw, P., Lalonde, F., Stockman, M., Wallace, G. L., Greenstein, D., … Giedd, J. N. (2011). How does your cortex grow? The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 31(19), 7174–7177.Google Scholar
Regis, J., Mangin, J. F., Ochiai, T., Frouin, V., Riviere, D., Cachia, A., … Samson, Y. (2005). “Sulcal root” generic model: A hypothesis to overcome the variability of the human cortex folding patterns. Neurologia Medico-Chirurgica (Tokyo), 45(1), 1–17.Google Scholar
Reiss, A. L., Abrams, M. T., Singer, H. S., Ross, J. L., & Denckla, M. B. (1996). Brain development, gender and IQ in children. A volumetric imaging study. Brain, 119(Pt 5), 1763–1774.Google Scholar
Reuter, M., Tisdall, M. D., Qureshi, A., Buckner, R. L., van der Kouwe, A. J. W., & Fischl, B. (2015). Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage, 107, 107–115.Google Scholar
Riahi, F., Zijdenbos, A., Narayanan, S., Arnold, D., Francis, G., Antel, J., & Evans, A. C. (1998). Improved correlation between scores on the expanded disability status scale and cerebral lesion load in relapsing-remitting multiple sclerosis. Results of the application of new imaging methods. Brain, 121(Pt 7), 1305–1312.Google Scholar
Richman, D. P., Stewart, R. M., Hutchinson, J. W., & Caviness, V. S. Jr. (1975). Mechanical model of brain convolutional development. Science, 189(4196), 18–21.Google Scholar
Rilling, J. K., & Insel, T. R. (1999). The primate neocortex in comparative perspective using magnetic resonance imaging. Journal of Human Evolution, 37(2), 191–223.Google Scholar
Ritchie, S. J., Booth, T., Valdes Hernandez, M. D., Corley, J., Maniega, S. M., Gow, A. J., … Deary, I. J. (2015). Beyond a bigger brain: Multivariable structural brain imaging and intelligence. Intelligence, 51, 47–56.Google Scholar
Riva, D., & Giorgi, C. (2000). The cerebellum contributes to higher functions during development: Evidence from a series of children surgically treated for posterior fossa tumours. Brain, 123(5), 1051–1061.Google Scholar
Román, F. J., Morillo, D., Estrada, E., Escorial, S., Karama, S., & Colom, R. (2018). Brain-intelligence relationships across childhood and adolescence: A latent-variable approach. Intelligence, 68, 21–29.Google Scholar
Roth, G., & Dicke, U. (2005). Evolution of the brain and intelligence. Trends in Cognitive Sciences, 9(5), 250–257.Google Scholar
Rushton, J. P., & Ankney, C. D. (2009). Whole brain size and general mental ability: A review. International Journal of Neuroscience, 119(5), 691–731.Google Scholar
Sanabria-Diaz, G., Melie-Garcia, L., Iturria-Medina, Y., Aleman-Gomez, Y., Hernandez-Gonzalez, G., Valdes-Urrutia, L., … Valdes-Sosa, P. (2010). Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks. Neuroimage, 50(4), 1497–1510.Google Scholar
Schmahmann, J. D. (2004). Disorders of the cerebellum: Ataxia, dysmetria of thought, and the cerebellar cognitive affective syndrome. The Journal of Neuropsychiatry and Clinical Neurosciences, 16(3), 367–378.Google Scholar
Schmitt, J. E., Neale, M. C., Clasen, L. S., Liu, S., Seidlitz, J., Pritikin, J. N., … Raznahan, A. (2019). A comprehensive quantitative genetic analysis of cerebral surface area in youth. Journal of Neuroscience, 39(16), 3028–3040.Google Scholar
Schmitt, J. E., Raznahan, A., Clasen, L. S., Wallace, G. L., Pritikin, J. N., Lee, N. R., … Neale, M. C. (2019). The dynamic associations between cortical thickness and general intelligence are genetically mediated. Cerebral Cortex, 29(11), 4743–4752.Google Scholar
Schoenemann, P. T., Budinger, T. F., Sarich, V. M., & Wang, W. S. Y. (2000). Brain size does not predict general cognitive ability within families. Proceedings of the National Academy of Sciences, 97(9), 4932–4937.Google Scholar
Schulte, T., & Muller-Oehring, E. M. (2010). Contribution of callosal connections to the interhemispheric integration of visuomotor and cognitive processes. Neuropsychology Review, 20(2), 174–190.Google Scholar
Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., … Giedd, J. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440(7084), 676–679.Google Scholar
Sowell, E. R., Thompson, P. M., Leonard, C. M., Welcome, S. E., Kan, E., & Toga, A. W. (2004). Longitudinal mapping of cortical thickness and brain growth in normal children. The Journal of Neuroscience, 24(38), 8223.Google Scholar
Stonnington, C. M., Tan, G., Klöppel, S., Chu, C., Draganski, B., Jack, C. R. Jr., … Frackowiak, R. S. (2008). Interpreting scan data acquired from multiple scanners: a study with Alzheimer’s disease. Neuroimage, 39(3), 1180–1185.Google Scholar
Storsve, A. B., Fjell, A. M., Tamnes, C. K., Westlye, L. T., Overbye, K., Aasland, H. W., & Walhovd, K. B. (2014). Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: Regions of accelerating and decelerating change. Journal of Neuroscience, 34(25), 8488–8498.Google Scholar
Stucht, D., Danishad, K. A., Schulze, P., Godenschweger, F., Zaitsev, M., & Speck, O. (2015). Highest resolution in vivo human brain MRI using prospective motion correction. PLoS One, 10(7), e0133921.Google Scholar
Sur, M., & Rubenstein, J. L. (2005). Patterning and plasticity of the cerebral cortex. Science, 310(5749), 805–810.Google Scholar
Tadayon, E., Pascual-Leone, A., & Santarnecchi, E. (2019). Differential contribution of cortical thickness, surface area, and gyrification to fluid and crystallized intelligence. Cerebral Cortex, 30(1).Google Scholar
Tamnes, C. K., Fjell, A. M., Østby, Y., Westlye, L. T., Due-Tønnessen, P., Bjørnerud, A., & Walhovd, K. B. (2011). The brain dynamics of intellectual development: Waxing and waning white and gray matter. Neuropsychologia, 49(13), 3605–3611.Google Scholar
Thompson, P. M., Hayashi, K. M., Dutton, R. A., Chiang, M.-C., Leow, A. D., Sowell, E. R., … Toga, A. W. (2007). Tracking Alzheimer’s disease. Annals of the New York Academy of Science, 1 097, 183–214.CrossRefGoogle Scholar
Thompson, P. (2020). ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries. Biological Psychiatry, 87(9, Suppl), S56.Google Scholar
Turin, G. (1960). An introduction to matched filters. IRE Transactions on Information Theory, 6(3), 311–329.Google Scholar
Van Essen, D. C. (2005). A population-average, landmark- and surface-based (PALS) atlas of human cerebral cortex. Neuroimage, 28(3), 635–662.Google Scholar
Vuoksimaa, E., Panizzon, M. S., Chen, C.-H., Fiecas, M., Eyler, L. T., Fennema-Notestine, C., … Kremen, W. S. (2015). The genetic association between neocortical volume and general cognitive ability is driven by global surface area rather than thickness. Cerebral Cortex, 25(8), 2127–2137.Google Scholar
Watson, P. D., Paul, E. J., Cooke, G. E., Ward, N., Monti, J. M., Horecka, K. M., … Barbey, A. K. (2016). Underlying sources of cognitive-anatomical variation in multi-modal neuroimaging and cognitive testing. Neuroimage, 129, 439–449.Google Scholar
Westerhausen, R., Friesen, C. M., Rohani, D. A., Krogsrud, S. K., Tamnes, C. K., Skranes, J. S., … Walhovd, K. B. (2018). The corpus callosum as anatomical marker of intelligence? A critical examination in a large-scale developmental study. Brain Structure and Function, 223(1), 285–296.Google Scholar
Westlye, L. T., Walhovd, K. B., Dale, A. M., Bjørnerud, A., Due-Tønnessen, P., Engvig, A., … Fjell, A. M. (2009). Life-span changes of the human brain white matter: Diffusion tensor imaging (DTI) and volumetry. Cerebral Cortex, 20(9), 2055–2068.Google Scholar
Wickett, J. C., Vernon, P. A., & Lee, D. H. (2000). Relationships between factors of intelligence and brain volume. Personality and Individual Differences, 29(6), 1095–1122.Google Scholar
Winkler, A. M., Kochunov, P., Blangero, J., Almasy, L., Zilles, K., Fox, P. T., … Glahn, D. C. (2010). Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage, 53(3), 1135–1146.Google Scholar
Winkler, A. M., Sabuncu, M. R., Yeo, B. T., Fischl, B., Greve, D. N., Kochunov, P., … Glahn, D. C. (2012). Measuring and comparing brain cortical surface area and other areal quantities. Neuroimage, 61(4), 1428–1443.Google Scholar
Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K. J., & Evans, A. C. (1996). A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping, 4(1), 58–73.Google Scholar
Xie, Y., Chen, Y. A., & De Bellis, M. D. (2012). The relationship of age, gender, and IQ with the brainstem and thalamus in healthy children and adolescents: A magnetic resonance imaging volumetric study. Journal of Child Neurology, 27(3), 325–331.Google Scholar
Zatorre, R. J., Fields, R. D., & Johansen-Berg, H. (2012). Plasticity in gray and white: Neuroimaging changes in brain structure during learning. Nature Neuroscience, 15(4), 528–536.Google Scholar
Zijdenbos, A. P., Lerch, J. P., Bedell, B. J., & Evans, A. C. (2005). Brain imaging in drug R&D. Biomarkers 10(Suppl 1), S58–S68.Google Scholar
Zilles, K., Armstrong, E., Schleicher, A., & Kretschmann, H. J. (1988). The human pattern of gyrification in the cerebral cortex. Anatomy and Embryology (Berlin), 179(2), 173–179.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×